Application of Regression Modeling to Data Observed Over Time
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | por eng |
Título da fonte: | Internext |
Texto Completo: | https://internext.espm.br/internext/article/view/477 |
Resumo: | The central idea of this text is to guide researchers through the application of regression modeling when the data under analysis are observed over time. In general, there are no doubts regarding the application of this modeling in cross sections. However, when there is dependence on the data over time, some care needs to be taken for the results to be reliable and have the same interpretation of the coefficients obtained using the least squares method. The text begins with a presentation of the concept of autocorrelation and partial autocorrelation to identify and apply autoregressive modeling. Following this approach, the Augmented Dickey-Fuller test for detecting stationarity is presented, an essential condition for the estimators of ordinary least squares to be consistent. The Granger causality test is also presented and an example of regression applied to the series of the Cost of Living Index and the National Price Index for General Consumers. All the examples are presented with the help of Microsoft Excel to universalize the technique. |
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Application of Regression Modeling to Data Observed Over TimeAplicação da Modelagem de Regressão em Dados Observados ao Longo do TempoLongitudinal dataStationarityAutoregressive modelsGranger causalityLagDados longitudinaisEstacionariedadeModelos autorregressivosCausalidade de GrangerDefasagemThe central idea of this text is to guide researchers through the application of regression modeling when the data under analysis are observed over time. In general, there are no doubts regarding the application of this modeling in cross sections. However, when there is dependence on the data over time, some care needs to be taken for the results to be reliable and have the same interpretation of the coefficients obtained using the least squares method. The text begins with a presentation of the concept of autocorrelation and partial autocorrelation to identify and apply autoregressive modeling. Following this approach, the Augmented Dickey-Fuller test for detecting stationarity is presented, an essential condition for the estimators of ordinary least squares to be consistent. The Granger causality test is also presented and an example of regression applied to the series of the Cost of Living Index and the National Price Index for General Consumers. All the examples are presented with the help of Microsoft Excel to universalize the technique.A ideia central deste texto é orientar o pesquisador a aplicar a modelagem de regressão quando os dados em análise foram observados ao longo do tempo. Em geral, não há dúvidas da aplicação dessa modelagem em seções transversais. Contudo, quando há dependência dos dados ao longo do tempo, alguns cuidados precisam ser tomados para que os resultados sejam confiáveis e valham as mesmas interpretações dos coeficientes obtidos via o método de mínimos quadrados. O texto inicia com a apresentação do conceito de autocorrelação e de autocorrelação parcial, a fim de identificar e aplicar a modelagem autorregressiva. Após essa abordagem, é apresentado o teste de Dickey-Fuller Aumentado para a detecção de estacionariedade, condição essencial para que os estimadores de mínimos quadrados ordinários sejam consistentes. Também é apresentado o teste de causalidade de Granger e um exemplo de regressão aplicado às séries do Índice de Custo de Vida e Índice de Preços ao Consumidor Amplo. Todos os exemplos foram apresentados com a ajuda do Microsoft Excel, a fim de universalizar a técnica.Escola Superior de Propaganda e Marketing - ESPM2018-09-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttps://internext.espm.br/internext/article/view/47710.18568/1980-4865.13342-50Internext - International Business and Management Review ; Vol. 13 No. 3 (2018): September/December; 42-50Internext; v. 13 n. 3 (2018): Setembro/Dezembro; 42-501980-4865reponame:Internextinstname:Escola Superior de Propaganda e Marketing (ESPM)instacron:ESPMporenghttps://internext.espm.br/internext/article/view/477/pdfhttps://internext.espm.br/internext/article/view/477/pdf_1Copyright (c) 2018 Internextinfo:eu-repo/semantics/openAccessFigueiredo, Cléber da CostaSilva, Aldy Fernandes da2023-06-06T20:47:27Zoai:ojs.emnuvens.com.br:article/477Revistahttps://internext.espm.br/internextPRIhttps://internext.espm.br/internext/oaiinternext@espm.br1980-48651980-4865opendoar:2023-06-06T20:47:27Internext - Escola Superior de Propaganda e Marketing (ESPM)false |
dc.title.none.fl_str_mv |
Application of Regression Modeling to Data Observed Over Time Aplicação da Modelagem de Regressão em Dados Observados ao Longo do Tempo |
title |
Application of Regression Modeling to Data Observed Over Time |
spellingShingle |
Application of Regression Modeling to Data Observed Over Time Figueiredo, Cléber da Costa Longitudinal data Stationarity Autoregressive models Granger causality Lag Dados longitudinais Estacionariedade Modelos autorregressivos Causalidade de Granger Defasagem |
title_short |
Application of Regression Modeling to Data Observed Over Time |
title_full |
Application of Regression Modeling to Data Observed Over Time |
title_fullStr |
Application of Regression Modeling to Data Observed Over Time |
title_full_unstemmed |
Application of Regression Modeling to Data Observed Over Time |
title_sort |
Application of Regression Modeling to Data Observed Over Time |
author |
Figueiredo, Cléber da Costa |
author_facet |
Figueiredo, Cléber da Costa Silva, Aldy Fernandes da |
author_role |
author |
author2 |
Silva, Aldy Fernandes da |
author2_role |
author |
dc.contributor.author.fl_str_mv |
Figueiredo, Cléber da Costa Silva, Aldy Fernandes da |
dc.subject.por.fl_str_mv |
Longitudinal data Stationarity Autoregressive models Granger causality Lag Dados longitudinais Estacionariedade Modelos autorregressivos Causalidade de Granger Defasagem |
topic |
Longitudinal data Stationarity Autoregressive models Granger causality Lag Dados longitudinais Estacionariedade Modelos autorregressivos Causalidade de Granger Defasagem |
description |
The central idea of this text is to guide researchers through the application of regression modeling when the data under analysis are observed over time. In general, there are no doubts regarding the application of this modeling in cross sections. However, when there is dependence on the data over time, some care needs to be taken for the results to be reliable and have the same interpretation of the coefficients obtained using the least squares method. The text begins with a presentation of the concept of autocorrelation and partial autocorrelation to identify and apply autoregressive modeling. Following this approach, the Augmented Dickey-Fuller test for detecting stationarity is presented, an essential condition for the estimators of ordinary least squares to be consistent. The Granger causality test is also presented and an example of regression applied to the series of the Cost of Living Index and the National Price Index for General Consumers. All the examples are presented with the help of Microsoft Excel to universalize the technique. |
publishDate |
2018 |
dc.date.none.fl_str_mv |
2018-09-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://internext.espm.br/internext/article/view/477 10.18568/1980-4865.13342-50 |
url |
https://internext.espm.br/internext/article/view/477 |
identifier_str_mv |
10.18568/1980-4865.13342-50 |
dc.language.iso.fl_str_mv |
por eng |
language |
por eng |
dc.relation.none.fl_str_mv |
https://internext.espm.br/internext/article/view/477/pdf https://internext.espm.br/internext/article/view/477/pdf_1 |
dc.rights.driver.fl_str_mv |
Copyright (c) 2018 Internext info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2018 Internext |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf application/pdf |
dc.publisher.none.fl_str_mv |
Escola Superior de Propaganda e Marketing - ESPM |
publisher.none.fl_str_mv |
Escola Superior de Propaganda e Marketing - ESPM |
dc.source.none.fl_str_mv |
Internext - International Business and Management Review ; Vol. 13 No. 3 (2018): September/December; 42-50 Internext; v. 13 n. 3 (2018): Setembro/Dezembro; 42-50 1980-4865 reponame:Internext instname:Escola Superior de Propaganda e Marketing (ESPM) instacron:ESPM |
instname_str |
Escola Superior de Propaganda e Marketing (ESPM) |
instacron_str |
ESPM |
institution |
ESPM |
reponame_str |
Internext |
collection |
Internext |
repository.name.fl_str_mv |
Internext - Escola Superior de Propaganda e Marketing (ESPM) |
repository.mail.fl_str_mv |
internext@espm.br |
_version_ |
1793890310075449344 |